Time-series analysis

In statistics and signal processing, a time series is a sequence of data points, measured typically at successive times, spaced apart at uniform time intervals. Time series analysis comprises methods that attempt to understand such time series, often either to understand the underlying theory of the data points (where did they come from? what generated them?), or to make forecasts (predictions). Time series prediction is the use of a model to predict future events based on known past events: to predict future data points before they are measured. The standard example is the opening price of a share of stock based on its past performance.

Models for time series data can have many forms. Two broad classes of practical importance are the moving average (MA) models, and the autoregressive (AR) models. These two classes are treated in more detail in the article on autoregressive moving average models (ARMA).

See also

linear prediction, anomaly time series, trend estimation, prediction interval